基于激光散斑的半稠密深度图获取算法

A Semi-Dense Depth Map Acquisition Algorithm Based on Laser Speckle

获取基于激光散斑的深度图像时存在匹配精度低、计算量大,以及在面对不同测量环境时鲁棒性差等问题,为此,提出了一种基于激光散斑的半稠密深度图获取算法。为解决鲁棒性差的问题,采用局部自适应二值化对散斑图像进行预处理,保证了窗口描述子的光照不变性;在测量精度方面,通过聚类算法提取出每个散斑的中心像素坐标,提高了每个散斑的位置准确度;在匹配成功率方面,将窗口描述子进行卷积得到简化的描述子,在减少计算量的同时增大了匹配成功率。最后根据匹配准则得到散斑配对点,再根据三角测距原理得到了每个散斑的深度值。实验结果表明:所提算法的鲁棒性较强,精度较高,提高了匹配成功率。

Depth map acquisition, which is based on laser speckle, presents some issues, such as low matching precision, large amount of calculation, and poor robustness in different measurement environments. In this paper, a semi-dense depth map acquisition algorithm based on laser speckle is proposed to address these issues. The problem of poor robustness can be solved using the locally adaptive binarization, which preprocesses the speckle map to ensure the illumination invariance of the window descriptor. In terms of measurement accuracy, the central pixel coordinates of each speckle are extracted using a clustering algorithm, which improves the positional accuracy of each speckle. Regarding the matching success rate issue, the window descriptor is convoluted to obtain a simplified descriptor, which is able to reduce the amount of calculations and increase the matching success rate. Finally, the speckle pairing points are obtained according to the matching criterion, and then the depth values of each speckle are obtained according to the triangulation principle. Experiments confirm that the proposed algorithm is highly robust and accurate and improves the matching success rate.